https://github.com/Faithzh/TFB
공식 코드Twofold Debiasing Enhances Fine-Grained Learning with Coarse Labels
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Learning fine-grained representation with coarsely-labelled dataset, which can significantly reduce the labelling cost. As a simple example, for the task of differentiation between different pets, we need a knowledgeable cat lover to distinguish between ‘British short’ and ‘Siamese’, but even a child annotator may help to discriminate between ‘cat’ and ‘non-cat’.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Twofold Debiasing Enhances Fine-Grained Learning with Coarse Labels
Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet
MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset
Fine-grained Angular Contrastive Learning with Coarse Labels
Weakly Supervised Representation Learning with Coarse Labels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.